Overview

Dataset statistics

Number of variables20
Number of observations12746
Missing cells0
Missing cells (%)0.0%
Duplicate rows296
Duplicate rows (%)2.3%
Total size in memory2.0 MiB
Average record size in memory168.0 B

Variable types

Categorical9
Numeric11

Alerts

Dataset has 296 (2.3%) duplicate rowsDuplicates
Breed1 is highly overall correlated with TypeHigh correlation
Dewormed is highly overall correlated with VaccinatedHigh correlation
Gender is highly overall correlated with QuantityHigh correlation
Quantity is highly overall correlated with GenderHigh correlation
Type is highly overall correlated with Breed1High correlation
Vaccinated is highly overall correlated with DewormedHigh correlation
Health is highly imbalanced (88.3%)Imbalance
Age has 179 (1.4%) zerosZeros
Breed2 has 9116 (71.5%) zerosZeros
Color2 has 3636 (28.5%) zerosZeros
Color3 has 8916 (70.0%) zerosZeros
Fee has 10880 (85.4%) zerosZeros
VideoAmt has 12230 (96.0%) zerosZeros
PhotoAmt has 262 (2.1%) zerosZeros

Reproduction

Analysis started2024-11-21 12:33:10.647908
Analysis finished2024-11-21 12:33:30.989418
Duration20.34 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.2 KiB
1
6485 
2
6261 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12746
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6485
50.9%
2 6261
49.1%

Length

2024-11-21T12:33:31.113176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T12:33:31.261351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 6485
50.9%
2 6261
49.1%

Most occurring characters

ValueCountFrequency (%)
1 6485
50.9%
2 6261
49.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6485
50.9%
2 6261
49.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6485
50.9%
2 6261
49.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6485
50.9%
2 6261
49.1%

Age
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3254354
Minimum0
Maximum20
Zeros179
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size199.2 KiB
2024-11-21T12:33:31.395212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile12
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.0665134
Coefficient of variation (CV)0.94013967
Kurtosis2.1112595
Mean4.3254354
Median Absolute Deviation (MAD)1
Skewness1.6610636
Sum55132
Variance16.536531
MonotonicityNot monotonic
2024-11-21T12:33:31.577690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2 3503
27.5%
1 2304
18.1%
3 1966
15.4%
4 1109
 
8.7%
12 967
 
7.6%
5 595
 
4.7%
6 558
 
4.4%
8 309
 
2.4%
7 281
 
2.2%
9 184
 
1.4%
Other values (11) 970
 
7.6%
ValueCountFrequency (%)
0 179
 
1.4%
1 2304
18.1%
2 3503
27.5%
3 1966
15.4%
4 1109
 
8.7%
5 595
 
4.7%
6 558
 
4.4%
7 281
 
2.2%
8 309
 
2.4%
9 184
 
1.4%
ValueCountFrequency (%)
20 32
 
0.3%
19 26
 
0.2%
18 165
 
1.3%
17 67
 
0.5%
16 50
 
0.4%
15 79
 
0.6%
14 76
 
0.6%
13 40
 
0.3%
12 967
7.6%
11 94
 
0.7%

Breed1
Real number (ℝ)

HIGH CORRELATION 

Distinct157
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272.76981
Minimum0
Maximum307
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size199.2 KiB
2024-11-21T12:33:31.768757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile141
Q1266
median266
Q3307
95-th percentile307
Maximum307
Range307
Interquartile range (IQR)41

Descriptive statistics

Standard deviation51.074521
Coefficient of variation (CV)0.18724404
Kurtosis8.443711
Mean272.76981
Median Absolute Deviation (MAD)41
Skewness-2.734598
Sum3476724
Variance2608.6067
MonotonicityNot monotonic
2024-11-21T12:33:31.963065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
307 5312
41.7%
266 3380
26.5%
265 1180
 
9.3%
299 308
 
2.4%
264 254
 
2.0%
292 233
 
1.8%
285 171
 
1.3%
141 149
 
1.2%
218 119
 
0.9%
254 90
 
0.7%
Other values (147) 1550
 
12.2%
ValueCountFrequency (%)
0 4
< 0.1%
1 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
11 2
 
< 0.1%
15 8
0.1%
16 1
 
< 0.1%
17 3
 
< 0.1%
18 5
< 0.1%
ValueCountFrequency (%)
307 5312
41.7%
306 54
 
0.4%
305 8
 
0.1%
304 7
 
0.1%
303 40
 
0.3%
301 1
 
< 0.1%
300 19
 
0.1%
299 308
 
2.4%
298 1
 
< 0.1%
297 5
 
< 0.1%

Breed2
Real number (ℝ)

ZEROS 

Distinct125
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.498431
Minimum0
Maximum307
Zeros9116
Zeros (%)71.5%
Negative0
Negative (%)0.0%
Memory size199.2 KiB
2024-11-21T12:33:32.142970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3202.75
95-th percentile307
Maximum307
Range307
Interquartile range (IQR)202.75

Descriptive statistics

Standard deviation124.2421
Coefficient of variation (CV)1.6456249
Kurtosis-0.68528496
Mean75.498431
Median Absolute Deviation (MAD)0
Skewness1.1067017
Sum962303
Variance15436.099
MonotonicityNot monotonic
2024-11-21T12:33:32.339987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9116
71.5%
307 1489
 
11.7%
266 558
 
4.4%
265 296
 
2.3%
299 128
 
1.0%
264 109
 
0.9%
292 89
 
0.7%
218 71
 
0.6%
141 69
 
0.5%
285 67
 
0.5%
Other values (115) 754
 
5.9%
ValueCountFrequency (%)
0 9116
71.5%
1 1
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
10 2
 
< 0.1%
14 1
 
< 0.1%
17 1
 
< 0.1%
18 3
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
307 1489
11.7%
306 24
 
0.2%
305 6
 
< 0.1%
304 1
 
< 0.1%
303 21
 
0.2%
302 1
 
< 0.1%
301 1
 
< 0.1%
300 9
 
0.1%
299 128
 
1.0%
296 2
 
< 0.1%

Gender
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.2 KiB
2
6144 
1
4540 
3
2062 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12746
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 6144
48.2%
1 4540
35.6%
3 2062
 
16.2%

Length

2024-11-21T12:33:32.528909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T12:33:32.656337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 6144
48.2%
1 4540
35.6%
3 2062
 
16.2%

Most occurring characters

ValueCountFrequency (%)
2 6144
48.2%
1 4540
35.6%
3 2062
 
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 6144
48.2%
1 4540
35.6%
3 2062
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 6144
48.2%
1 4540
35.6%
3 2062
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 6144
48.2%
1 4540
35.6%
3 2062
 
16.2%

Color1
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1792719
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.2 KiB
2024-11-21T12:33:32.788076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7169536
Coefficient of variation (CV)0.78785652
Kurtosis1.2306161
Mean2.1792719
Median Absolute Deviation (MAD)0
Skewness1.537611
Sum27777
Variance2.9479297
MonotonicityNot monotonic
2024-11-21T12:33:32.918965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 6551
51.4%
2 3103
24.3%
3 751
 
5.9%
5 687
 
5.4%
6 566
 
4.4%
4 560
 
4.4%
7 528
 
4.1%
ValueCountFrequency (%)
1 6551
51.4%
2 3103
24.3%
3 751
 
5.9%
4 560
 
4.4%
5 687
 
5.4%
6 566
 
4.4%
7 528
 
4.1%
ValueCountFrequency (%)
7 528
 
4.1%
6 566
 
4.4%
5 687
 
5.4%
4 560
 
4.4%
3 751
 
5.9%
2 3103
24.3%
1 6551
51.4%

Color2
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2709085
Minimum0
Maximum7
Zeros3636
Zeros (%)28.5%
Negative0
Negative (%)0.0%
Memory size199.2 KiB
2024-11-21T12:33:33.059801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.7307521
Coefficient of variation (CV)0.83486042
Kurtosis-1.5080329
Mean3.2709085
Median Absolute Deviation (MAD)2
Skewness0.17177048
Sum41691
Variance7.4570069
MonotonicityNot monotonic
2024-11-21T12:33:33.197390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 3636
28.5%
7 2962
23.2%
2 2923
22.9%
6 943
 
7.4%
5 921
 
7.2%
4 765
 
6.0%
3 596
 
4.7%
ValueCountFrequency (%)
0 3636
28.5%
2 2923
22.9%
3 596
 
4.7%
4 765
 
6.0%
5 921
 
7.2%
6 943
 
7.4%
7 2962
23.2%
ValueCountFrequency (%)
7 2962
23.2%
6 943
 
7.4%
5 921
 
7.2%
4 765
 
6.0%
3 596
 
4.7%
2 2923
22.9%
0 3636
28.5%

Color3
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9316648
Minimum0
Maximum7
Zeros8916
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size199.2 KiB
2024-11-21T12:33:33.337625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0067464
Coefficient of variation (CV)1.556557
Kurtosis-0.98200819
Mean1.9316648
Median Absolute Deviation (MAD)0
Skewness0.96910018
Sum24621
Variance9.0405241
MonotonicityNot monotonic
2024-11-21T12:33:33.483571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 8916
70.0%
7 2801
 
22.0%
5 365
 
2.9%
6 342
 
2.7%
4 171
 
1.3%
3 151
 
1.2%
ValueCountFrequency (%)
0 8916
70.0%
3 151
 
1.2%
4 171
 
1.3%
5 365
 
2.9%
6 342
 
2.7%
7 2801
 
22.0%
ValueCountFrequency (%)
7 2801
 
22.0%
6 342
 
2.7%
5 365
 
2.9%
4 171
 
1.3%
3 151
 
1.2%
0 8916
70.0%

MaturitySize
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.2 KiB
2
8992 
1
2902 
3
 
840
4
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12746
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8992
70.5%
1 2902
 
22.8%
3 840
 
6.6%
4 12
 
0.1%

Length

2024-11-21T12:33:33.638362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T12:33:33.781504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 8992
70.5%
1 2902
 
22.8%
3 840
 
6.6%
4 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 8992
70.5%
1 2902
 
22.8%
3 840
 
6.6%
4 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 8992
70.5%
1 2902
 
22.8%
3 840
 
6.6%
4 12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 8992
70.5%
1 2902
 
22.8%
3 840
 
6.6%
4 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 8992
70.5%
1 2902
 
22.8%
3 840
 
6.6%
4 12
 
0.1%

FurLength
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.2 KiB
1
7789 
2
4459 
3
 
498

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12746
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7789
61.1%
2 4459
35.0%
3 498
 
3.9%

Length

2024-11-21T12:33:33.934403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T12:33:34.072533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 7789
61.1%
2 4459
35.0%
3 498
 
3.9%

Most occurring characters

ValueCountFrequency (%)
1 7789
61.1%
2 4459
35.0%
3 498
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7789
61.1%
2 4459
35.0%
3 498
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7789
61.1%
2 4459
35.0%
3 498
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7789
61.1%
2 4459
35.0%
3 498
 
3.9%

Vaccinated
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.2 KiB
2
6963 
1
4386 
3
1397 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12746
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 6963
54.6%
1 4386
34.4%
3 1397
 
11.0%

Length

2024-11-21T12:33:34.226915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T12:33:34.364377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 6963
54.6%
1 4386
34.4%
3 1397
 
11.0%

Most occurring characters

ValueCountFrequency (%)
2 6963
54.6%
1 4386
34.4%
3 1397
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 6963
54.6%
1 4386
34.4%
3 1397
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 6963
54.6%
1 4386
34.4%
3 1397
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 6963
54.6%
1 4386
34.4%
3 1397
 
11.0%

Dewormed
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.2 KiB
1
6794 
2
4620 
3
1332 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12746
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 6794
53.3%
2 4620
36.2%
3 1332
 
10.5%

Length

2024-11-21T12:33:34.525782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T12:33:34.660838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 6794
53.3%
2 4620
36.2%
3 1332
 
10.5%

Most occurring characters

ValueCountFrequency (%)
1 6794
53.3%
2 4620
36.2%
3 1332
 
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6794
53.3%
2 4620
36.2%
3 1332
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6794
53.3%
2 4620
36.2%
3 1332
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6794
53.3%
2 4620
36.2%
3 1332
 
10.5%

Sterilized
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.2 KiB
2
9438 
1
1913 
3
1395 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12746
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 9438
74.0%
1 1913
 
15.0%
3 1395
 
10.9%

Length

2024-11-21T12:33:34.814278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T12:33:34.941695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 9438
74.0%
1 1913
 
15.0%
3 1395
 
10.9%

Most occurring characters

ValueCountFrequency (%)
2 9438
74.0%
1 1913
 
15.0%
3 1395
 
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 9438
74.0%
1 1913
 
15.0%
3 1395
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 9438
74.0%
1 1913
 
15.0%
3 1395
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 9438
74.0%
1 1913
 
15.0%
3 1395
 
10.9%

Health
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.2 KiB
1
12407 
2
 
319
3
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12746
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 12407
97.3%
2 319
 
2.5%
3 20
 
0.2%

Length

2024-11-21T12:33:35.094998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T12:33:35.235437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 12407
97.3%
2 319
 
2.5%
3 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 12407
97.3%
2 319
 
2.5%
3 20
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12407
97.3%
2 319
 
2.5%
3 20
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12407
97.3%
2 319
 
2.5%
3 20
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12407
97.3%
2 319
 
2.5%
3 20
 
0.2%

Quantity
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6289816
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.2 KiB
2024-11-21T12:33:35.360887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum20
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4823257
Coefficient of variation (CV)0.90997078
Kurtosis23.269201
Mean1.6289816
Median Absolute Deviation (MAD)0
Skewness3.8586596
Sum20763
Variance2.1972895
MonotonicityNot monotonic
2024-11-21T12:33:35.535465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 9571
75.1%
2 1266
 
9.9%
3 695
 
5.5%
4 507
 
4.0%
5 315
 
2.5%
6 177
 
1.4%
7 83
 
0.7%
8 50
 
0.4%
9 32
 
0.3%
10 16
 
0.1%
Other values (9) 34
 
0.3%
ValueCountFrequency (%)
1 9571
75.1%
2 1266
 
9.9%
3 695
 
5.5%
4 507
 
4.0%
5 315
 
2.5%
6 177
 
1.4%
7 83
 
0.7%
8 50
 
0.4%
9 32
 
0.3%
10 16
 
0.1%
ValueCountFrequency (%)
20 4
 
< 0.1%
18 1
 
< 0.1%
17 3
 
< 0.1%
16 3
 
< 0.1%
15 4
 
< 0.1%
14 2
 
< 0.1%
13 2
 
< 0.1%
12 5
 
< 0.1%
11 10
0.1%
10 16
0.1%

Fee
Real number (ℝ)

ZEROS 

Distinct67
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.8689
Minimum0
Maximum3000
Zeros10880
Zeros (%)85.4%
Negative0
Negative (%)0.0%
Memory size199.2 KiB
2024-11-21T12:33:35.742239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile100
Maximum3000
Range3000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation70.888923
Coefficient of variation (CV)3.9671677
Kurtosis280.49511
Mean17.8689
Median Absolute Deviation (MAD)0
Skewness10.654215
Sum227757
Variance5025.2394
MonotonicityNot monotonic
2024-11-21T12:33:35.963551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10880
85.4%
50 414
 
3.2%
100 336
 
2.6%
200 148
 
1.2%
150 140
 
1.1%
20 124
 
1.0%
30 95
 
0.7%
300 68
 
0.5%
1 67
 
0.5%
10 64
 
0.5%
Other values (57) 410
 
3.2%
ValueCountFrequency (%)
0 10880
85.4%
1 67
 
0.5%
5 24
 
0.2%
8 6
 
< 0.1%
9 4
 
< 0.1%
10 64
 
0.5%
14 1
 
< 0.1%
15 20
 
0.2%
20 124
 
1.0%
25 6
 
< 0.1%
ValueCountFrequency (%)
3000 1
 
< 0.1%
1000 3
 
< 0.1%
800 1
 
< 0.1%
750 6
< 0.1%
700 4
< 0.1%
688 1
 
< 0.1%
650 4
< 0.1%
600 9
0.1%
599 1
 
< 0.1%
550 4
< 0.1%

State
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41345.672
Minimum41324
Maximum41415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.2 KiB
2024-11-21T12:33:36.152948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum41324
5-th percentile41326
Q141326
median41326
Q341401
95-th percentile41401
Maximum41415
Range91
Interquartile range (IQR)75

Descriptive statistics

Standard deviation32.242235
Coefficient of variation (CV)0.00077982127
Kurtosis-0.72015917
Mean41345.672
Median Absolute Deviation (MAD)0
Skewness1.1199252
Sum5.2699194 × 108
Variance1039.5617
MonotonicityNot monotonic
2024-11-21T12:33:36.343529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
41326 7456
58.5%
41401 3206
25.2%
41327 729
 
5.7%
41336 430
 
3.4%
41330 358
 
2.8%
41332 222
 
1.7%
41324 107
 
0.8%
41325 95
 
0.7%
41335 76
 
0.6%
41361 23
 
0.2%
Other values (4) 44
 
0.3%
ValueCountFrequency (%)
41324 107
 
0.8%
41325 95
 
0.7%
41326 7456
58.5%
41327 729
 
5.7%
41330 358
 
2.8%
41332 222
 
1.7%
41335 76
 
0.6%
41336 430
 
3.4%
41342 11
 
0.1%
41345 16
 
0.1%
ValueCountFrequency (%)
41415 3
 
< 0.1%
41401 3206
25.2%
41367 14
 
0.1%
41361 23
 
0.2%
41345 16
 
0.1%
41342 11
 
0.1%
41336 430
 
3.4%
41335 76
 
0.6%
41332 222
 
1.7%
41330 358
 
2.8%

VideoAmt
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.058920446
Minimum0
Maximum8
Zeros12230
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size199.2 KiB
2024-11-21T12:33:36.493469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.34542924
Coefficient of variation (CV)5.862638
Kurtosis108.36405
Mean0.058920446
Median Absolute Deviation (MAD)0
Skewness8.8753283
Sum751
Variance0.11932136
MonotonicityNot monotonic
2024-11-21T12:33:36.674381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 12230
96.0%
1 380
 
3.0%
2 81
 
0.6%
3 29
 
0.2%
4 15
 
0.1%
5 7
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 12230
96.0%
1 380
 
3.0%
2 81
 
0.6%
3 29
 
0.2%
4 15
 
0.1%
5 7
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
6 2
 
< 0.1%
5 7
 
0.1%
4 15
 
0.1%
3 29
 
0.2%
2 81
 
0.6%
1 380
 
3.0%
0 12230
96.0%

PhotoAmt
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0010984
Minimum0
Maximum30
Zeros262
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size199.2 KiB
2024-11-21T12:33:36.886392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile11
Maximum30
Range30
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.5566368
Coefficient of variation (CV)0.88891511
Kurtosis12.048442
Mean4.0010984
Median Absolute Deviation (MAD)2
Skewness2.798385
Sum50998
Variance12.649665
MonotonicityNot monotonic
2024-11-21T12:33:37.091490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 2535
19.9%
3 2136
16.8%
2 2032
15.9%
5 1873
14.7%
4 1623
12.7%
6 560
 
4.4%
7 395
 
3.1%
8 285
 
2.2%
0 262
 
2.1%
9 210
 
1.6%
Other values (21) 835
 
6.6%
ValueCountFrequency (%)
0 262
 
2.1%
1 2535
19.9%
2 2032
15.9%
3 2136
16.8%
4 1623
12.7%
5 1873
14.7%
6 560
 
4.4%
7 395
 
3.1%
8 285
 
2.2%
9 210
 
1.6%
ValueCountFrequency (%)
30 17
0.1%
29 6
 
< 0.1%
28 7
0.1%
27 4
 
< 0.1%
26 10
0.1%
25 8
0.1%
24 12
0.1%
23 10
0.1%
22 9
0.1%
21 14
0.1%

AdoptionSpeed
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.2 KiB
2
3579 
4
3311 
3
2794 
1
2711 
0
 
351

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12746
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 3579
28.1%
4 3311
26.0%
3 2794
21.9%
1 2711
21.3%
0 351
 
2.8%

Length

2024-11-21T12:33:37.289943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T12:33:37.463743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 3579
28.1%
4 3311
26.0%
3 2794
21.9%
1 2711
21.3%
0 351
 
2.8%

Most occurring characters

ValueCountFrequency (%)
2 3579
28.1%
4 3311
26.0%
3 2794
21.9%
1 2711
21.3%
0 351
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3579
28.1%
4 3311
26.0%
3 2794
21.9%
1 2711
21.3%
0 351
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3579
28.1%
4 3311
26.0%
3 2794
21.9%
1 2711
21.3%
0 351
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3579
28.1%
4 3311
26.0%
3 2794
21.9%
1 2711
21.3%
0 351
 
2.8%

Interactions

2024-11-21T12:33:28.300414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:12.345480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:13.965225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:15.590554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:17.142075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:18.666460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:20.305041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:22.028355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:23.672548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:25.216996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:26.716423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:28.454569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:12.503051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:14.120093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:15.739914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:17.286366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:18.808666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:20.461242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:22.209723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:23.824217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:25.361985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:26.865109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:28.588566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:12.645157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:14.250170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:15.891421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:17.426177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:18.949611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:20.619476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:22.381160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:23.971496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:25.498313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:27.013112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:28.728413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:12.785851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:14.384665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:16.013730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:17.555300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:19.082586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:20.781364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:22.538300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:24.105699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:25.625304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:27.163246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:28.853475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:12.931720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:14.514291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:16.174468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:17.695773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:19.221361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:20.950361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:22.675112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:24.249430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:25.760566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:27.303185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:28.999989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:13.082133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:14.660882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:16.312782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:17.822345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:19.365163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:21.113880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:22.815756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:24.384789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:25.896279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:27.452676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:29.130512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:13.236562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:14.792689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:16.456956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:17.967325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:19.505447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:21.266066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:22.963876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:24.527885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:26.029159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:27.586224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:29.276157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:13.391424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:14.939736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:16.600244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:18.104097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:19.681101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:21.434559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:23.106493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:24.660980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:26.179298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:27.736655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:29.405663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:13.529207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:15.068944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:16.732801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:18.252695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:19.829748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:21.582588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:23.251452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:24.801163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:26.319000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:27.872092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:29.539337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:13.671767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:15.304180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:16.852727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:18.382787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:19.978167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:21.731785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:23.383256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:24.925582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:26.434878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:28.010470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:29.676253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:13.823590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:15.452990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:17.005466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:18.522558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:20.156470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:21.875696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:23.539956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:25.073867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:26.578322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-21T12:33:28.149673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-11-21T12:33:37.645779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AdoptionSpeedAgeBreed1Breed2Color1Color2Color3DewormedFeeFurLengthGenderHealthMaturitySizePhotoAmtQuantityStateSterilizedTypeVaccinatedVideoAmt
AdoptionSpeed1.0000.2120.136-0.025-0.033-0.0370.0010.0690.0200.0810.0560.0210.053-0.0640.0670.0310.1570.1120.097-0.014
Age0.2121.000-0.161-0.0110.142-0.010-0.0840.2000.1010.0770.1340.0250.100-0.062-0.2220.0440.3560.1070.266-0.018
Breed10.136-0.1611.000-0.140-0.050-0.150-0.1130.113-0.1110.1050.0840.0100.169-0.041-0.002-0.0480.0600.8430.1630.017
Breed2-0.025-0.011-0.1401.000-0.029-0.0080.0250.0690.0490.1270.0740.0150.0730.0620.049-0.0530.0610.4150.097-0.018
Color1-0.0330.142-0.050-0.0291.000-0.084-0.3000.0530.0520.0460.1250.0000.042-0.049-0.1570.0220.0460.3110.0680.000
Color2-0.037-0.010-0.150-0.008-0.0841.0000.0950.0720.0010.0240.1990.0000.0580.0810.0600.0230.0410.3720.0810.024
Color30.001-0.084-0.1130.025-0.3000.0951.0000.078-0.0070.0190.2210.0000.0490.1140.327-0.0000.0390.2240.0800.014
Dewormed0.0690.2000.1130.0690.0530.0720.0781.000-0.1170.0200.1510.0430.081-0.1030.1790.0040.4350.1280.735-0.036
Fee0.0200.101-0.1110.0490.0520.001-0.007-0.1171.0000.1500.0270.0000.0600.001-0.077-0.0210.0130.0370.0570.008
FurLength0.0810.0770.1050.1270.0460.0240.0190.0200.1501.0000.0110.0060.1130.004-0.029-0.0530.0360.0810.018-0.010
Gender0.0560.1340.0840.0740.1250.1990.2210.1510.0270.0111.0000.0150.0750.0830.5680.0210.0840.1180.1250.000
Health0.0210.0250.0100.0150.0000.0000.0000.0430.0000.0060.0151.0000.041-0.014-0.0280.0210.0450.0240.047-0.002
MaturitySize0.0530.1000.1690.0730.0420.0580.0490.0810.0600.1130.0750.0411.0000.015-0.071-0.0810.0570.2560.0920.013
PhotoAmt-0.064-0.062-0.0410.062-0.0490.0810.114-0.1030.0010.0040.083-0.0140.0151.0000.148-0.0210.0500.0500.0500.153
Quantity0.067-0.222-0.0020.049-0.1570.0600.3270.179-0.077-0.0290.568-0.028-0.0710.1481.0000.0340.0850.1020.128-0.004
State0.0310.044-0.048-0.0530.0220.023-0.0000.004-0.021-0.0530.0210.021-0.081-0.0210.0341.0000.0320.1460.030-0.022
Sterilized0.1570.3560.0600.0610.0460.0410.0390.4350.0130.0360.0840.0450.0570.0500.0850.0321.0000.0420.471-0.018
Type0.1120.1070.8430.4150.3110.3720.2240.1280.0370.0810.1180.0240.2560.0500.1020.1460.0421.0000.209-0.005
Vaccinated0.0970.2660.1630.0970.0680.0810.0800.7350.0570.0180.1250.0470.0920.0500.1280.0300.4710.2091.000-0.032
VideoAmt-0.014-0.0180.017-0.0180.0000.0240.014-0.0360.008-0.0100.000-0.0020.0130.153-0.004-0.022-0.018-0.005-0.0321.000

Missing values

2024-11-21T12:33:30.370920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-21T12:33:30.775825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TypeAgeBreed1Breed2GenderColor1Color2Color3MaturitySizeFurLengthVaccinatedDewormedSterilizedHealthQuantityFeeStateVideoAmtPhotoAmtAdoptionSpeed
0232990117011222111004132601.02
12126501120223331104140102.00
21130701270221121104132607.03
3143070212021112111504140108.02
41130701100212221104132603.02
52326602560212221104132602.02
6212264264110023223113004132603.01
71030702127212221604132609.03
82226502600222221104132606.01
921226502170223331104132602.04
TypeAgeBreed1Breed2GenderColor1Color2Color3MaturitySizeFurLengthVaccinatedDewormedSterilizedHealthQuantityFeeStateVideoAmtPhotoAmtAdoptionSpeed
149791630701170211111104132605.04
149811830702270222221104132602.03
1498222266021472121213041336016.03
149831330701127221121104132607.02
149862126602567212121104140101.03
149871619502170131121104140101.00
149882226603100222221404132603.02
149902226526635673221315304132605.03
149912926602470111111104133603.04
14992113073071200212221104133201.03

Duplicate rows

Most frequently occurring

TypeAgeBreed1Breed2GenderColor1Color2Color3MaturitySizeFurLengthVaccinatedDewormedSterilizedHealthQuantityFeeStateVideoAmtPhotoAmtAdoptionSpeed# duplicates
63113073072120222221104132601.036
11312307021202122211204132701.036
62113073072120222121104132601.035
871230701200212221104132601.025
1161230702120213321104132602.025
16112307027002122211204132701.025
171123073071200221121104132601.025
1611307012002122211204132701.024
70113073072270222121104132603.014
891230701200221121104132601.024